港口航道与近海工程2024，Vol.61Issue(3)：86-90,5.DOI:10.16403/j.cnki.ggjs20240318
一种基于智能算法的GNSS高程拟合方法
A GNSS Elevation Fitting Method Based on Intelligent Algorithm
王朝 1王志文1
作者信息
- 1. 中交第一航务工程勘察设计院有限公司,天津 300222
- 折叠
摘要
Abstract
Generalized regression neural network(GRNN)is a new feed-forward neural network which has many advantages such as fewer training times,short period,strong forecasting capability of nonlinear parameters and etc.However,as the only adjustable parameter of GRNN,SPREAD cannot be obtained automatically,which limits its further application.To solve this drawback,Fly Optimization Algorithm(FOA)is combined with GRNN to build FOAGRNN model,which optimizes GRNN model and achieves automatic gathering of adjustable parameter.In order to test the accuracy of GNSS elevation fitting based on FOAGRNN model,an experimental analysis is carried out.The results show that the above accuracy of GNSS height fitting reaches 6 mm.FOAGRNN model is also compared with plane fitting model and quadric fitting model,which shows that the superiority of FOAGRNN model in fitting accuracy.In conclusion,FOAGRNN model supports higher accuracy of GNSS height fitting even though less data samples are available.关键词
果蝇优化算法(FOA)/广义回归神经网络(GRNN)/GNSS高程拟合Key words
Fly Optimization Algorithm(FOA)/Generalized Regression Neural Network(GRNN)/GNSS elevation fitting分类
天文与地球科学引用本文复制引用
王朝,王志文..一种基于智能算法的GNSS高程拟合方法[J].港口航道与近海工程,2024,61(3):86-90,5.
